Abstract P3-02-09: Do radiographic features influence the decision to order a breast MRI? A prospective cohort study
Notice bibliographique
Résumé
Abstract Background: The clinical impact of breast MRI in the neoadjuvant setting is unclear. It is assumed that MRI may help with surgical planning. Factors that may affect whether an MRI is ordered for breast cancer in the neoadjuvant setting may include both imaging and tumour characteristics. Literature suggests MRI can be used in the neoadjuvant therapy (NAT) if there is evidence of high density of breast tissue, multifocal disease, multi-centric disease, lymph node involvement or presence of calcifications. In a non-trial setting, it is unclear when MRI is ordered and if it is indeed ordered based on the above imaging criteria. We sought to determine how MRI is currently implemented in a provincial practice to determine which patients are selected for MRI prior to NAT. Specifically, we aimed to determine if the imaging characteristics determined likelihood of use of MRI in the neoadjuvant setting. Methods: Patients who received neoadjuvant therapy between May 2012 and May 2016 were captured in a prospective database at the BC Cancer Agency in Vancouver. Patients were reviewed and identified as those who either received a breast MRI or not. A random sample of 80 cases, 40 who received MRI and 40 who did not, was taken from this database. Charts were reviewed in detail, and detailed review of the radiographic features from mammogram and ultrasound imaging reports was recorded. Results: 80 patients were reviewed in detail. There were no differences in patient demographics or tumour characteristics. Imaging review demonstrated no statistical significant difference in use of MRI based on reported density, multi-centric disease, calcifications, and nodal involvement. The only radiographic feature that was different was presence of multifocal disease on conventional imaging, where 40.0% of patients who had an MRI had multifocal disease reported whereas only 17.5% of those who did not have an MRI had multifocality reported (p = 0.03). Discussion/Conclusions: Despite radiographic guidelines for use of MRI, the decision by the ordering physician regarding who should receive an MRI prior to NAT still appears to be unsystematic. This could be due to incorrect interpretation of radiographic reports by the ordering physicians and lack of availability or access to the interpreting radiologist. Results suggest that the ordering physician is already aware of multifocal disease and is utilizing MRI to verify this presence, rather than using MRI to investigate the possibility of multifocality in dense breast tissue. Based on this strategy of use it is unlikely that MRI will reduce the rate of mastectomy in this patient population. Table 1: Summary of radiographic features in MRI vs. non-MRI cohorts MRI (N=40)No MRI (N=40)Chi SquareDensity (C or D)25 (62.5%)16 (40.0%)p=0.11Multifocal Disease16 (40.0%)7 (17.5%)p=0.03Multi-centric Disease7 (17.5%)3 (7.5%)p=0.19Lymph Node Involvement31 (77.5%)25 (62.5%)p=0.25Calcifications Present23 (57.5%)21 (52.5%)p=0.56 Citation Format: Illmann C, Simmons CE, McDermott M, Xu J, Wilson C. Do radiographic features influence the decision to order a breast MRI? A prospective cohort study [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr P3-02-09.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».